1. Package and some annotations

2. Preprocessing

3. Suicide

3.2 Suicide number

4.1 Suicide rate associated with sex

4.2 Suicide number associated with sex

5.1 Suicide propotion of all age

5.2 Suicide Number in each Age

7.1 Suicides change in different continent

7.2 Suicides number in different continent

1. Package and some annotations

#Replace age 1 represent for 5-14 years
#Replace age 2 represent for 15-24 years
#Replace age 3 represent for 25-34 years
#Replace age 4 represent for 35-54 years
#Replace age 5 represent for 55-74 years
#Replace age 6 represent for 75+ years
library(tidyverse)
library(dplyr)
library(ggplot2)
library(countrycode)
library(rworldmap)
library(animation)
library(gganimate)
library(leaflet)
library(rgeos)
library(rworldmap)
library(maps)
library(tidyverse)
library(rworldxtra)

2. Preprocessing

data <- read.csv("master.csv",stringsAsFactors = FALSE )
data <- as.data.frame(data)
#rename 嚜盧ountry into country
names(data)[1] <- "country"
# convert country into continent
data$continent <- countrycode(sourcevar = data[,"country"],
                              origin = "country.name",
                              destination = "continent")

data <- data %>%
  filter(year != 2016)

3. Suicide

 data_suirate<- data %>%
   select(year, suicides_million_pop) %>%
   group_by(year) %>%
   summarise(sum_sui=sum(suicides_million_pop))

 ggplot(data_suirate, aes(x=year, y=sum_sui))+
   geom_line(position = "stack", color = "dodgerblue3", size = 1.5)+
   geom_point(size = 2, color = "orangered")+
   labs(title = "Suicide Rate")+
   theme_grey(base_size = 20)

  data_suirate<- data %>%
   select(year, suicides_no) %>%
   group_by(year) %>%
   summarise(sum_sui=sum(suicides_no))

 ggplot(data_suirate, aes(x=year, y=sum_sui))+
   geom_col( fill = "dodgerblue3")+
   labs(title = "Suicide Number")+
   theme_grey(base_size = 20)

4. Suicide associated with sex

data_year_sex <- data %>%
  select(year, sex, suicides_no) %>%
  arrange(year, sex) %>%
  group_by(year,sex) %>%
  summarise(sum_suicides_no = sum(suicides_no))


ggplot(data_year_sex, aes(x=year,y=sum_suicides_no,fill = sex))+
  geom_col(position = "fill") +
  #scale_fill_brewer(palette = "Set1")+
   scale_fill_manual(values=c("dodgerblue3", "darkorange"))+
  labs(title = "Suicide Rate Male versus Female")+
  theme_grey(base_size = 20)

data_year_sex <- data %>%
  select(year, sex, suicides_million_pop) %>%
  arrange(sex, year) %>%
  group_by(sex,year) %>%
  summarise(sum_suicides_rate = sum(suicides_million_pop))


ggplot(data_year_sex, aes(year,sum_suicides_rate, color = sex))+
  geom_line(size = 1.5)+
  scale_colour_manual(values=c("dodgerblue3", "darkorange")) +
  geom_point(size = 2)+
  labs(title = "Suicide Rate Male versus Female")+
  theme_grey(base_size = 20)

5. Suicide associated with age

data_year_sui <- data %>%
  select(year, age, suicides_million_pop) %>%
  arrange(year, age) %>%
  group_by(year, age) %>%
  summarise(sum = sum(suicides_million_pop))


ggplot(data_year_sui, aes(x=year, y=sum, fill=age))+
  geom_col(position = "fill")+
  labs(title = "Suicide propotion of all age")+
  theme_grey(base_size = 20)

data_eachage <- data %>%
  select(year, suicides_million_pop, age) %>%
  group_by(age, year) %>%
  summarise(sum_suirate=sum(suicides_million_pop))

ggplot(data_eachage, aes(year, sum_suirate, color = age)) +
  geom_line() +
  geom_point() +
  facet_grid(age~.)+
  labs(title = "Suicide Number in each Age", color = "Age")+
  theme_grey(base_size = 20)

6. Suicides associated with gdp

data_gdp <- data %>%
  select(year, gdp_per_capita, suicides_million_pop, population)

temp <- ggplot(data_gdp, aes(x=gdp_per_capita, y=suicides_million_pop, size = population))+
 geom_point()+
 transition_time(year)+
 labs(title = 'Date: {frame_time}')+
 theme_grey(base_size = 20)
 animate(temp, fps = 2)

7. Suicides in different continent

data_con <- data %>%
 select(year, suicides_no, continent) %>%
 group_by(year, continent) %>%
 summarise(sum_con = sum(suicides_no))
ggplot(data_con, aes(continent, sum_con, fill = continent))+
 geom_col()+
 scale_fill_brewer(palette = "Blues") +
 transition_time(year) +
 labs(title = "Date: {frame_time}")+
 theme_grey(base_size = 20)

data_con <- data %>%
  select(continent, year, suicides_million_pop) %>%
  group_by(continent, year) %>%
  summarise(sum_sui_rate = sum(suicides_million_pop))

ggplot( data_con, aes(year, sum_sui_rate, col = continent)) +
  geom_line( size = 1.5)+
  labs(title = "Suicide rate each continent")+
  theme_grey(base_size = 20)

8. Suicides number of different country in 2000

data_country <- data %>%
  filter(year == 2000) %>%
  select(country, suicides_million_pop) %>%
  group_by(country) %>%
  summarise(sum_sui_rate = sum(suicides_million_pop)) %>%
  arrange(desc(sum_sui_rate))

data_country$country <- factor(data_country$country,
                               ordered = T,
                               levels = rev(data_country$country))

ggplot(data_country, aes(country,sum_sui_rate, fill=country)) +
  geom_col()+
  scale_fill_viridis_d()+
  coord_flip()+
  theme(legend.position = "none")+
  labs(title = "Suicide rate each country")

9. Suicide number on world map

 saveGIF({
  for( i in 1987:2015 ){
     data_continent <- data %>%
       filter(year == i) %>%
       select(suicides_no, country) %>%
       group_by(country) %>%
       summarise(sum_con=sum(suicides_no))

    #oceanCol="lightblue", missingCountryCol="grey65"
     par(mai=c(0,0,0.5,0),xaxs="i",yaxs="i")
     sPDF <- joinCountryData2Map( data_continent, joinCode = "NAME" ,nameJoinColumn = "country")
     mapCountryData( sPDF,
                     mapTitle = i,
                     nameColumnToPlot="sum_con",
                     catMethod = c(0, 5000, 10000, 15000, 20000, 25000, 30000, 35000, 40000, 45000),
                     colourPalette = "white2Black",)
  }}
  ,interval = .2
  ,movie.name="test.gif"
  )

## Output at: test.gif

 knitr::include_graphics("test.gif")

10. Suicide number on world map with leaflet

# Get world map
wmap <- getMap(resolution = 'high')

# Get centroids of each countries
centroids <- gCentroid(wmap, byid = TRUE)
df <- as.data.frame(centroids)

# Since there are some countries with different name in two data,
# we adjust them to the same names.
rownames(df)[rownames(df) == "The Bahamas"] <- "Bahamas"
rownames(df)[rownames(df) == "United States of America"] <- "United States"
rownames(df)[rownames(df) == "Russia"] <- "Russian Federation"
rownames(df)[rownames(df) == "Cape Verde"] <- "Cabo Verde"
rownames(df)[rownames(df) == "South Korea"] <- "Republic of Korea"
rownames(df)[rownames(df) == "Macau S.A.R"] <- "Macau"
rownames(df)[rownames(df) == "Republic of Serbia"] <- "Serbia"
rownames(df)[rownames(df) == "Saint Vincent and the Grenadines"] <- "Saint Vincent and Grenadines"

# Load suicide data
master <- read.csv('master.csv')
names(master)[1] <- "country"
total_countries <- (unique(master['country']))
total_countries <- total_countries %>% pull(country)

# Calculate suicides per million population
data_year_sui <- master %>%
  select(country, year, age, suicides_million_pop) %>%
  arrange(year, age) %>%
  group_by(country, year) %>%
  summarise(sum = sum(suicides_million_pop)) 

data_year_sui$country = as.character(data_year_sui$country)

# Save longitude and latitude
country_cor <- data.frame(country=character(),
                          lat=double(), 
                          lng=double(),
                          suicides=character(),
                          stringsAsFactors=FALSE) 

for(name in total_countries) {
  if(name %in% rownames(df)) {
    col <- data_year_sui %>% filter(country == name)
    content = name
    for(i in seq(nrow(col))) {
      row <- paste((col$year)[i], (col$sum)[i])
      content = paste(sep = "<br/>", content, row)
    }
    country_cor <- country_cor %>% add_row(country = name, lat = df[name, 'y'], lng = df[name, 'x'], suicides = content)
  } 
}


# Create Interactive map
leaflet(data=country_cor) %>%
  addTiles() %>%
  addMarkers (lat=country_cor$lat, lng=country_cor$lng, popup=country_cor$suicides)